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Environmental performance evaluation of agro-industrial innovations

e

Part 2:

methodological approach for performing vulnerability analysis of watersheds

Maria Cléa Brito de Figueirêdo

a,

*

, Geraldo Stachetti Rodrigues

b

, Armando Caldeira-Pires

c

, Morsyleide de

Freitas Rosa

a

, Fernando Antônio Sousa de Aragão

a

, Vicente de Paulo Pereira Barbosa Vieira

d

,

Francisco Suetônio Bastos Mota

d

aEmbrapa Agroindústria Tropical, Rua Dra Sara Mesquita, 2270-Planalto do Pici, CEP 60.511-110, Fortaleza, Ceará, Brazil

bEmbrapa Labex Europe/Unité Performance des systèmes de culture de plantes pérennes (Cirad PerSyst), TA-B 34/02, Bureau 143, Avenue Agropolis, 34398 Montpellier Cedex.5, France

cUniversidade de Brasília, Campus Universitário Darcy Ribeiro, CEP 70910-900, Brasília, Distrito Federal, Brazil dUniversidade Federal do Ceará Bloco 713, Campus do Pici, CEP 60451-970, Fortaleza, Ceará, Brazil

a r t i c l e

i n f o

Article history:

Received 17 August 2009 Received in revised form 12 March 2010 Accepted 22 April 2010 Available online 21 May 2010

Keywords:

Multi-criteria analysis Innovation

Ambitec-Life Cycle Environmental impact

a b s t r a c t

Environmental vulnerability analysis has been sparsely used in environmental performance evaluation (EPE) of technological innovations. The present paper proposes a methodological approach to carry out vulnerability analysis of watersheds and to integrate this analysis into methods of environmental performance evaluation of agro-industrial innovations. This approach is applied to the Ambitec-Life Cycle method, described in Part 1 (this issue) of this study. The case study of green coconut substrate compared to ripe coconut substrate, also described in Part 1 (this issue), is now presented considering the vulnerability analysis of the watersheds where the life cycle stages of these products occur. The inte-gration of vulnerability analysis in Ambitec-Life Cycle contributes to a better understanding of the environmental aspects of agro-industrial technological innovations with potential to cause significant impacts in watersheds where these innovations are implemented.

Ó2010 Elsevier Ltd. All rights reserved.

1. Introduction

The environmental performance evaluation (EPE) of

agro-indus-trial innovations is an important step in the technological innovation

process. Numerous methods are available to conduct the EPE of

technological innovations. Among these, some speci

cally address

agricultural and agro-industrial innovations, such as the

Ambitec-Agro (Rodrigues et al., 2003) and the Inova-tec (Jesus-Hitzschky,

2007) approaches, routinely applied in product or process

evalua-tion at the Brazilian Agency for Agricultural Research

e

Embrapa. A

more general approach to EPE is offered by life cycle assessment (LCA)

methods such as the Ecoindicator 99 (Goedkoop and Spriensma,

2000), IMPACT 2002

þ

(Jolliet et al., 2003), EPS (Steen, 1999), TRACI

(Bare, 2003) and EDIP 2003 (Potting and Hauschild, 2005).

Each of these methods presents specially appropriated scopes

and sets of selected indicators, but none of them is directly

designed to consider the vulnerability of local environments where

technological innovations are implemented. The use of

vulnera-bility analysis is still in its beginning in the environmental

perfor-mance arena. According to Kværner et al. (Kværner et al., 2006),

vulnerability studies are hardly carried out during project or

technological evaluations, despite their potential to enlighten the

very de

nition of impacts to be considered and the decisions on

appropriate projects or technological alternatives in impact reports.

For example, an agro-industrial product or process that requires

large amounts of water should not be developed or adopted in

a semi-arid region. In such an environment, water scarcity, soil

salinization, and deserti

cation are restraining vulnerability

aspects that impose important qualitative and quantitative

considerations, e.g., how restraining is water availability? At which

level do these resource limitations impede resource extraction for

production and discharge of processing residues?

In fact, vulnerability studies have been developed in the last

decade to support planning decisions in the most varied scopes and

scales such as the vulnerability of regions to climate change

(Metzger et al., 2006), mountain areas to environmental

degrada-tion (Li et al., 2006), aquifers to pesticide and nitrate contaminadegrada-tion

(Barreto, 2006), geosystems to morphological processes (Lima et al.,

* Corresponding author. Tel.:þ55 85 3391 7100; fax:þ55 85 3391 7109.

E-mail addresses:clea@cnpat.embrapa.br(M.C. Brito de Figueirêdo),stacheti@

cnpma.embrapa.br (G.S. Rodrigues), armandcp@unb.br (A. Caldeira-Pires),

morsy@cnpat.embrapa.br (M. de Freitas Rosa), aragao@cnpat.embrapa.br (F.A.

Sousa de Aragão),vpvieira@ufc.br(V. de Paulo Pereira Barbosa Vieira),suetonio@

ufc.br(F.S. Bastos Mota).

Contents lists available at

ScienceDirect

Journal of Cleaner Production

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / j c l e p r o

0959-6526/$esee front matterÓ2010 Elsevier Ltd. All rights reserved.

doi:10.1016/j.jclepro.2010.04.013

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2000), regions to global change (Schoter et al., 2004; Metzger et al.,

2006), suburban areas to industrial pollution

(Tixier et al., 2005),

watersheds to environmental degradation (Tran et al., 2002;

Zielinski, 2002), reservoirs to eutrophication (Bennion et al., 2005;

Figueirêdo et al., 2007), and ecosystems to environmental

degra-dation (Villa and McLeod, 2002).

As approached in these studies, vulnerability analysis has been

related to speci

c environmental concerns (climate change, erosion,

water pollution, etc.) and applied at different scales (aquifers,

watersheds, geosystems, ecosystems, etc.), denoting a variety of

concepts, applications, and scopes of currently available literature.

The objective of the methodological research detailed in the

present study is to propose an objective, quantitative approach to

allow the analysis of watershed vulnerability and to integrate this

analysis into EPE methods devoted to agro-industrial technological

innovations. In that sense, this approach is applied to the newly

developed Ambitec-Life Cycle, a multi-criteria method that

considers life cycle thinking in the environmental performance

evaluation of agro-industrial innovations (Part 1 (this issue)). The

case study of green coconut substrate (GCS), compared to ripe

coconut substrate (RCS) (Part 1 (this issue)), is now presented

considering the vulnerability analysis of four Brazilian watersheds

where the life cycle stages of these products occur. An analysis is

performed to point out which environmental performance

indi-cators have more potential to cause impacts on these particular

watersheds, considering their environmental vulnerability.

2. Methodological approach to evaluate the environmental

vulnerability of watersheds

2.1. Vulnerability concept and scale

In order to insert vulnerability analysis as a quantitative

procedure in the EPE of agro-industrial innovations, the

vulnera-bility conceptualization advocated by Adger (Adger, 2006) and

Gallopin (Gallopin, 2006) has been adapted, so that environmental

vulnerability is understood as the susceptibility of a watershed to

degradation, evaluated by considering the local environment’s:

-

exposure

to pressures, related to materials and energy

consumption and pollutant emissions. The consumptions and

emissions observed are those commonly associated with

agro-industrial activities, but that can also be inherent to other

activities that have the potential to cause environmental impacts

in the watershed (e.g.: water demand, wastewater and solid

waste generation);

-

sensitivity

to exerted pressures, evaluated by observing the

main physical and biotic environmental characteristics (e.g.:

soil types, climate, biodiversity) that occur in the watershed

and interact with the considered pressures, making the system

more or less vulnerable to such pressures;

-

capacity of response, evaluated by the adoption of conservation

measures by a local society that may enhance the watershed

capability to better respond to the exerted pressures. It is also

evaluated by the awareness and capability of the local society

to understand and act upon the exposure and sensitivity of the

local environment (e.g.: sewage facilities, water storage, and

delimitation of conservation areas).

The scale in a vulnerability study can be delimited in a range of

spatial or socio-economic levels (ecosystem, geosystem, watershed,

neighborhood, territory, etc.) according to the objectives of the

study. However, the watershed is specially suited to vulnerability

studies because human activities and associated technologies can

directly alter a watershed’s water quantity and quality, or change

soil and vegetation characteristics that affect water resources.

2.2. Multi-criteria structure

A literature review on sets of indicators related to

agro-indus-trial environmental issues (Figueirêdo, 2008; Monteiro and

Rodrigues, 2006) revealed that those most frequently relevant in

a watershed context are: biodiversity loss, soil erosion, compaction,

salinization, sodi

cation, acidi

cation, deserti

cation,

agrochemi-cals contamination, solid wastes, water scarcity, and water

pollu-tion. Thus, vulnerability indicators that allowed objective

expression of a watershed exposure, sensitivity and capacity of

response to these issues were chosen for the present study. An

important additional consideration for the selection and

organi-zation of indicators was the availability of reliable data for public

consultation in of

cial databases. The indicators were organized

under the criteria of exposure, sensitivity, and capacity of response.

A hierarchical multi-criteria structure based on the proposed

vulnerability concept is presented in

Fig. 1. The description of each

indicator is presented in

Appendix A.

Because indicator variables are usually represented by different

measurement units, normalization to a common dimensionless

scale is a typical step in order to allow aggregation in criteria and in

a

nal integrated index. The vulnerability scale used ranges from 1

(low vulnerability) to 2 (high vulnerability). The rules used to

perform the appropriate normalization and aggregation steps

necessary for this index development are described in Figueirêdo et

al (Figueirêdo et al., 2009).

2.3. Strategy for considering vulnerability analysis in the EPE of

agro-industrial innovations

According to Malczewski (Malczewski, 1999), the EPE can be

assessed using multi-criteria analysis where a set of environmental

indicators are related to criteria or objectives and these to a

nal

EPE index, that is used to support decisions about technological

innovation adoption. Ambitec-Agro (Monteiro and Rodrigues,

2006), Inova-tec (Jesus-Hitzschky, 2007), and Ambitec-Life Cycle

(Figueirêdo, 2008) are examples of EPE methods that use

multi-criteria schemes to study the possible impacts of agro-industrial

innovations. Although each EPE method based on multi-criteria

analysis has different hierarchical structures connecting indicators,

criteria, and a

nal index, and uses particular rules for

normaliza-tion and aggreganormaliza-tion, they all are based on measurable indicators

that can be weighed by a vulnerability index. This vulnerability

index can in turn function as a correction factor to translate

a watershed susceptibility to studied environmental pressures.

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Weighed indicator

i

¼

EPE indicator

i

*

EVI

(1a)

Weighed indicator

i

¼

EPE indicator

i

*

EVI

1

(1b)

In Equations

(1a) and (1b),

‘EPE_indicator

i

is the normalized value

of an indicator

‘i’

in a particular EPE method that can cause relevant

impact at the watershed level;

‘EVI’

is the dimensionless

normal-ized value representing the environmental vulnerability of the

watershed where the EPE of an innovation is being carried out; and

‘Weighed_indicator’

is the new modi

ed value of an EPE indicator

‘i’

that was weighed by the watershed vulnerability index. The use

of equation 1a can result in a score higher than the range used by an

environmental performance multi-criteria method and, in this case,

the maximum original score adopted by the method shall prevail.

3. Application of the proposed strategy to Ambitec-Life Cycle

The strategy proposed to insert vulnerability analysis in the EPE

of innovations has been applied to Ambitec-Life Cycle, the method

presented in Part 1 (this issue) of this study. This method evaluates

the environmental performance of an agro-industrial technological

innovation as compared with a current product or process, using

a set of performance indicators organized in principles, criteria,

and in a total environmental performance index. Ambitec-Life Cycle

considers four life cycle stages of an innovation: (i) production/

extraction of raw materials and resources used by the innovation;

(ii) innovation production process; (iii) innovation utilization; and

(iv)

nal disposal.

The integration of vulnerability analysis within Ambitec-Life

Cycle expands its framework, as presented in

Fig. 3. At the left side

of this

gure, vulnerability analysis was introduced for each stage of

the innovation as well as for its current product or process. The

results of vulnerability analysis are then used in the environmental

performance evaluation of the innovation and of its current product

or process, performed in each of their life cycle stages and in their

total analysis. At the right side of

Fig. 3, a new step was introduced

in Ambitec-Life Cycle original framework (described in Part 1 (this

issue)) to carry out the vulnerability analysis and to integrate the

environmental vulnerability index (EVI) in the procedures of

environmental performance evaluation.

3.1. Case study of EPE of an agro-industrial innovation using the

Ambitec-Life Cycle method expanded with vulnerability analysis

The proposed expanded framework of Ambitec-Life Cycle was

applied to identify the in

uence of the watershed vulnerability in

the performance evaluation of the agro-industrial innovation

‘Green

coconut substrate’

(GCS), as compared to the current product

‘Ripe

coconut substrate’

(RCS). Both substrates act as a physical support to

seedlings and to plant production in soilless cultivation. The life

cycle stages involved in the study of these products were: (i) coconut

husks disposal (stage 1), (ii) substrate production (stage 2), (iii)

substrate use in rose seedling production (stage 3a) and in rose

production (stage 3b), and (iv) substrate

nal disposal in a

com-posting area (stage 4). Part 1 (this issue) of this study presents details

of the environmental performance evaluation of GCS and RCS.

In this case study the values of environmental performance

indicators to each life cycle stage of these products were obtained

in production units located at four different watersheds:

Metro-politana, Parnaíba, Litoral, and Baixo Mundaú (Table 1). All these

watersheds are located in the Brazilian Northeast, a region

Fig. 2.Strategy for integrating the vulnerability analysis in the EPE of agro-industrial

innovations.

Fig. 1.Set of watershed environmental vulnerability indicators, organized in a multi-criteria structure.

M.C. Brito de Figueirêdo et al. / Journal of Cleaner Production 18 (2010) 1376e1385

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characterized by semi-arid climate with rainfall concentrated in

few months of the year and a great part of its population presenting

low income and literacy rates.

3.1.1. Vulnerability analysis of watersheds

The results of this analysis are shown in

Table 2. Vulnerability

analysis was performed in these watersheds using available

data-bases as well as minimum and maximum values to quantitative

indicators that are appropriate to the Brazilian environmental

condition, presented in

Appendix B.

The EVI obtained for these watersheds ranged from 1.52 in Baixo

Mundaú to 1.57 in Metropolitana watershed. Although the

varia-tion range was narrow among the watersheds, important

differ-ences in the vulnerability criteria were perceived. For instance,

three of the studied watersheds presented low vulnerability to

exposure (Metropolitana-1.35, Litoral-1.29, and Parnaíba-1.24),

with just Baixo Mundaú achieving a medium exposure (1.48),

mainly explained by the important agricultural sector observed in

this watershed. Sensitivity varied from low (Baixo Mundaú-1.34) to

medium (Litoral-1.59) to high (Metropolitana-1.61 and

Parnaíba-1.68). Higher sensitivity is related to higher rainfall intensity and

climate aridity (especially in Parnaíba). All watersheds presented

low capacity of response and consequently high vulnerability in

this criterion (Metropolitana-1.73, Litoral-1.78, Parnaíba-1.73 and

Baixo Mundaú-1.74), mostly related with modest investments in

conservation units and low water availability.

3.1.2. Integration of vulnerability analysis in the EPE of GCS and RCS

The EVIs of these watersheds were integrated into the

perfor-mance evaluation of GCS and RCS, according to the framework

presented for Ambitec-Life Cycle in

Fig. 3. It is worth to recall that

the performance scale of Ambitec-Life Cycle ranges from 0 (worst

performance) to 100 (best performance).

The EPE results of these products obtained by using the

Ambi-tec-Life Cycle without vulnerability analysis (Part 1 (this issue) of

this study) are compared with the EPE results using the expanded

version of this method in

Fig. 4. In the

rst situation, since

vulnerability was not considered, it was as if all watersheds

Fig. 3.Framework of Ambitec-Life Cycle, considering vulnerability analysis.

Table 1

Watersheds related to each life cycle stage of GCS and RCS. TECHNOLOGY STAGE 1-Coconut substrate

disposal

STAGE 2-Substrate production

STAGE 3a- Substrate use in rose seedling production

STAGE 3b - Substrate use in rose production

STAGE 4 - Substratefinal disposal

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Table 2

Results of the vulnerability analysis of four Brazilian watersheds.

Environmental

VulnerabilityeMetropolitana

Watershed (State of Ceará)

Environmental VulnerabilityeLitoral

Watershed (State of Ceará)

Environmental VulnerabilityeBaixo

Mundaú Watershed (State of Alagoas)

Environmental VulnerabilityeParnaíba

Watershed (State of Ceará)

Criteria Indicators Weight of Indicators

Weight of criteria

Indicators Criteria EVIa Indicators Criteria EVIa Indicators Criteria EVIa Indicators Criteria EVIa

1. Exposure 1.1 Agriculture activity

0.2 0.33 1.33 1.35 1.57 1.32 1.29 1.55 1.65 1.48 1.52 1.06 1.24 1.55

1.2 Industrial activity 0.2 1.09 1.01 1.00 1.00 1.3 Wastewater

generation per person

0.2 1.48 1.13 1.42 1.19

1.4 Waste generation per person

0.2 1.78 1.97 1.71 1.90

1.5 Water demand per person

0.2 1.06 1.01 1.60 1.06

Total¼ 1

2. Sensibility 2.1 Priority areas for conservation

0.2 0.33 1.47 1.61 1.40 1.59 1.41 1.34 1.52 1.68 2.2 Agriculture

suitability

0.2 1.63 1.58 1.28 1.69

2.3 Rainfall intensity 0.2 1.80 1.80 1.78 1.87 2.4 Irrigation water

quality

0.2 1.54 1.50 1.14 1.61

2.5 Climate aridity 0.2 1.63 1.65 1.12 1.72 Total¼ 1

3. Capacity of Response

3.1 Areas in conservation units

0.14 0.33 1.99 1.73 2.00 1.78 1.99 1.74 1.99 1.73 3.2 Adoption of

conservation practices

0.14 1.78 1.88 1.92 1.89

3.3 Potable water 0.14 1.57 1.58 1.45 1.41 3.4 Solid waste

disposal

0.14 1.65 1.68 1.66 1.58

3.5 Wastewater treatment

0.14 1.80 1.93 1.79 1.86

3.6 Water availability per person

0.14 2.00 2.00 1.99 1.99

3.7 Municipality Human Development Index (HDI-M)

0.14 1.34 1.38 1.40 1.36

Total¼ 1 1

aEVI

¼Environmental Vulnerability Index.

80.38

70.43

93.18

73.79 74.80

91.19 99.55

85.75

64.06 75,42

60,07 74,30

65.23

89,06 96,49

94,44 92,71

100

39,78

78,22 76,44

73,92 85.33

35,75

0

20

40

60

80

100

STAGE 1 -Coconut husk

disposal

STAGE 2 -Substrate production

STAGE 3a -Substrate use in

rose seedling production

STAGE 3b -Substrate use in rose production

STAGE 4 -Substrate final

disposal

TOTAL EVALUATION

GCS (without vulnerability analysis)

GCS (with vulnerability analysis)

RCS (without vulnerability analysis)

RCS (with vulnerability analysis)

Fig. 4.Environmental performance along the life cycle stages of GCS and RCS without and with vulnerability analysis integration.

M.C. Brito de Figueirêdo et al. / Journal of Cleaner Production 18 (2010) 1376e1385

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involved in the study presented the lowest vulnerability (equal to

one). When vulnerability analysis was introduced, since the EVI

scores of the involved watersheds were around 1.5, the

environ-mental performance index of both products were reduced, in all

stages of their life cycles and in the total EPE stage.

The total EPE of the studied products performed with

vulnera-bility analysis showed that when all stages are considered, the

performance of GCR was lower than the performance of RCS. This

occurred because the average EVI for GCS, considering the

vulner-ability of the watersheds involved in each one of the GCS life cycle

stages (Table 1), was higher (1.56) than the average EVI obtained for

RCS (1.54). This result corroborated the result obtained by the

evaluation without vulnerability analysis (Part 1), where RCS

showed advantages over GCS.

Analyzing the performance of the studied products at each life

cycle stage, the GCS continued to perform higher than RCS in stages

1 and 3a, and lower in stages 2, 3b and 4, when vulnerability was

considered. To understand which environmental aspects of GCS

have more potential to cause positive and negative impacts in the

studied watersheds, each life cycle stage is analyzed next.

3.1.2.1. Effects of vulnerability analysis in stage 1 (coconut husk

dis-posal).

In stage 1, the vulnerability analysis showed that the EVI of

the Metropolitana watershed, where the green coconut husks were

disposed in a land

ll, is higher than the EVI of Litoral, where ripe

coconut husks were incorporated into agricultural soils. These

vulnerability results reinforce the comparative advantage of using

green instead of ripe husks as raw material for the production of

substrate, since green husks are diverted from land

lls.

The use of green coconut husks implies that there will be no

disposal of this material in land

lls and, consequently, no leachate

generation and lower water levels, fossil fuels and energy

consumption. The generation of leachate, by green coconut husk

anaerobic degradation, has great potential to contribute to water

pollution in the Metropolitana watershed that already presents

medium vulnerability (1.48) to wastewater generation and high

vulnerability (1.80) to wastewater treatment (Table 2). The

consumption of water should also be avoided in the Metropolitana

watershed that presents low water availability per person (2.0).

3.1.2.2. Effects of vulnerability analysis in stage 2 (substrate

production).

In stage 2, GCS performed lower than RCS when

vulnerability was considered, since the EVI of Metropolitana, where

GCS was produced, was higher (1.57) than the EVI of Baixo Mundaú

(1.52), where RCS was produced. Considering that the production of

GCS presented higher consumption of water and higher generation

of solid wastes and ef

uent polluting load than RCS (Part 1), the

potential impact of these aspects in a watershed, such as

Metro-politana, was higher. In this watershed, water availability was

already very low, wastewater treatment and waste facilities were

insuf

cient and waste generation was higher (Table 2).

3.1.2.3. Effects of vulnerability analysis in stages 3 (substrate use) and

4 (substrate disposal).

In stage 3a (substrate use in rose seedling

production), both products were adopted by the same production

unit located in the Parnaíba watershed. Although the performance

of these products was reduced with vulnerability analysis, the

advantage of GCS over RCS was maintained. Despite GCS’

better

performance, it is important to notice that the impact of water

consumption (used in the washing of GCS before rose seeding

production) was signi

cant in Parnaíba which presented high

vulnerability to climate aridity and water availability (Table 2).

Stages 3b (substrate use in rose production) and 4 also occurred in

the same production unit, located in the Parnaíba watershed. The

performance of both products decreased, but the GCS continued to

perform lower than the RCS. Attention should be given to the high

volume of water demanded by GCS and high ef

uent polluting load

generated when irrigation water was drained and the GCS was

washed to reduce its electrical conductivity in rose production. These

environmental aspects have great potential to contribute to water

scarcity and pollution in the Parnaíba watershed, which presented

high vulnerability to water availability and wastewater treatment

(Table 2).

In stage 4, the larger amount of GCS refused after two years of

rose production required larger areas for composting

elds. Care

should be taken in the choice of the composting area to avoid using

good quality agricultural or forested areas. Parnaíba watershed

presented high sensitivity and vulnerability to agriculture

suit-ability and medium sensitivity and vulnersuit-ability to priority areas

for conservation.

3.1.2.4. Overall effects of vulnerability analysis in the EPE of GCS and

RCS.

The integration of vulnerability analysis in the environmental

performance of GCS shows that the environmental performance

indicators that received low scores, related to water consumption

and waste and ef

uent generation, present great potential to

signi

cantly impact the studied watersheds (Metropolitana, Litoral,

and Parnaíba). These watersheds are already vulnerable to the

occurrence of water scarcity events and water pollution, as well as

contamination by inadequate solid waste disposal. Further research

to reduce water consumption, as well as to reduce emission and

reuse ef

uents and wastes generated along GCS life cycle stages,

shall be carried out by R&D teams in order to improve the

inno-vation performance and to minimize its pressure upon natural

resources in the involved watersheds.

Because these substrates can be produced and used in seedling

and plant production all over Brazil, especially in the coastal areas,

vulnerability analyses should be carried out in the other

water-sheds. This action would shed light on which watersheds GCS

would probably cause smaller impacts, before research actions

de

ne new processes and product characteristics that could

improve its environmental performance.

4. Discussion and conclusion

Any attempt to measure vulnerability implies judgments about

which issues to include and which threshold values to adopt. This

fact, however, does not reduce scienti

c debates and attempts to

develop methods for conducting vulnerability analysis, that have

been increasingly used in a range of disciplines, as a tool for

deci-sion making (Adger, 2006). In the environmental area, different

interpretations of this term and measurement methods have been

developed, especially to help prioritization of actions in regions

affected by global change (Metzger et al., 2006; Schoter et al.,

2004), by ecosystem change (Villa and McLeod, 2002), and by

water eutrophication (Bennion et al., 2005; Figueirêdo et al., 2007).

The main bene

ts of adopting the proposed environmental

vulnerability strategy is the knowledge gained about which

envi-ronmental aspects (covered by performance indicators) could most

probably cause signi

cant impact in the natural resources at the

watershed level. Without vulnerability analysis, an EPE method

indicates critical environmental aspects of an organization

manage-ment system, a project, an activity or an innovation, but hardly

correlates these aspects with impacts in a regional scale. This

corre-lation may better support decisions about which aspects to prioritize

in management actions.

EPE methods based on qualitative analysis (e.g., methods based

on descriptive checklists or Battelle Columbus matrices (Canter,

1996) do not bene

t from the proposed strategy, since indicators

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team that conducts EPE studies based on such methods, because it

allows a better understanding of the surrounding environment

where innovation can be adopted. The accomplishment of

a vulnerability analysis always attracts the attention of the EPE

team in a research institute to those characteristics of a watershed

that make it more vulnerable and, consequently, facilitate the

shaping of innovations to perceived environmental constraints.

Acknowledgements

The authors would like to thank the Brazilian Agricultural

Research Corporation (Embrapa), for

nancially supporting the

research, and the researchers Adriano Lincoln Albuquerque Mattos

and Renato Carrha Leitão, for revision and important contributions

to this work.

Appendix A. Description of vulnerability indicators.

Indicators Description Calculation procedure 1.1 Agricultural

activities

This indicator evaluates the pressure exerted by agriculture and animal husbandry in a watershed. Agriculture activities represent a pressure factor in a watershed due to deforestation and land clearing, which can lead to biodiversity loss, cause soil degradation (erosion, salinization, and sodification) through inappropriate plowing and irrigation techniques, and can contribute to climate change and environmental contamination by agrochemicals.

Agriculture_activityi¼(agriculture_areai/municipality_areai)*100

Where‘agriculture_areai’is the area (in hectares) devoted to crops, pastures, and silviculture in municipality‘i’of the studied watershed;‘municipality_areai’is the area of municipality‘i’in hectares, and‘agriculture_activityi’, the percentage area of municipality‘i’devoted to agriculture. These data are commonly published at government agriculture agencies and the indicator ranges from 0% (minimum value) to 100% (maximum value).

1.2 Industrial activities

This indicator evaluates the pressure exerted by industry in a watershed. Industrial activities are important pressure factors, due to release of contaminants that can pollute soil, air, and water, as well as cause deforestation, biodiversity losses, and soil degradation (mainly extractive industry).

Industrial_activityi¼employed_peoplei/municipality_areai

Where‘employed_peoplei’means the number of people employed in extractive and transformation industries in municipality‘i’in the studied watershed;

‘municipality_areai’is the area of a municipality‘i’and‘Industrial_activityi’, the number of people employed in these industries by area unit in the watershed. These data are usually researched by government statistic offices.

1.3 Wastewater generation per person

This indicator computes the intensity of wastewater generation in a watershed. This indicator is nearly related to the water quality level in a watershed, since more wastewaters require larger water volumes to dilute pollutants, a critical problem especially in semi-arid regions. Generation of large amounts of wastewaters in a watershed is expected to cause water pollution, especially in regions where there is no appropriate treatment facilities.

Wastewater_generationi¼collected_volumei(m3/year)/population_reachedi(hab)

Where‘collected_volumei’is the total sewage volume collected by year in municipality‘i’in the studied watershed;‘population_reachedi’is the number of people with access to wastewater treatment in municipality‘i’, and

‘Wastewater_generationi’is the wastewater volume generated by person in municipality‘i’.

1.4 Solid waste generation per person

This indicator evaluates the intensity of solid waste generation in a watershed. Even when correctly disposed (landfills or incinerators), solid wastes can emit odors, global warming gases, and effluents rich in organic and inorganic pollutants. Solid waste generation leads to soil and water pollution in a watershed, especially in regions where appropriate solid waste treatment facilities are scarce.

Solid_waste_generationi¼collected_massi(kg/day)/population_reachedi(hab)

Where‘collected_massi’is the mass of solid wastes collected by day in municipality‘i’in the studied watershed;‘population_reachedi’is the number of people with access to solid wastes collection in municipality‘i’;

‘Solid_waste_generationi’is the mass of solid wastes generated per person in municipality‘i’.

1.5 Water demand per person

This indicator evaluates the intensity of water demand in a watershed. Water demand represents the volume of water required by population, agriculture, animal husbandry and industry in a watershed. Elevated water demands impact water reserves in a watershed, contributing to supply shortage events.

Water_demand¼watershed_water_demand(m3/year)/watershed_population

(hab)

Where‘watershed_water_demand’is the volume of water demanded by all users in a watershed (population, husbandry, agriculture and industry) in a year;

‘watershed_population’is the population of all municipalities in the watershed, and‘Water_demand’is the yearly amount of water demanded per person in the watershed.

2.1 Priority areas for conservation

This indicator evaluates the existence and extension of priority areas for biodiversity conservation in a watershed. Main areas for conservation are those with relevant biodiversity (occurrence of endemism, of rare or endangered species, of migratory species, and of traditional cultural sites) and high sensitivity to degradation.

These areas are classified infive conservation priority classes (MMA, 2006), and were attributed vulnerability values (ranging from 1 to 2 in the adopted scale) as follows: (i) not prioritized (vulnerability¼1.2); (ii) insufficiently known (vulnerability¼1.4); (iii) high priority vulnerability¼1.6); (iv) very high priority (vulnerability¼1.8) and (v) extreme priority (vulnerability¼2).

2.2 Agriculture suitability

This indicator evaluates the feasibility of agriculture in an area based on studies of soils susceptibility to erosion, compaction, acidification and salinization; soil characteristics such as depth, drainage, texture, permeability, salts and organic material concentration; precipitation; topography, and vegetation type. As agriculture suitability studies classify soil types in groups, according to agriculture suitability, each group was attributed to a vulnerability value.

The defined agriculture suitability groups and their adopted vulnerability values are: group 1, soils suited for agriculture (vulnerability¼1); group 2, soils regularly

suited for agriculture (vulnerability¼1.2); group 3, soils with restricted suitability for agriculture (vulnerability¼1.4); group 4, soils with good, regular or restricted suitability for planted pasture (vulnerability¼1.6); group 5, soils with good, regular, or restricted suitability for silviculture or natural pasture

(vulnerability¼1.8) and group 6, soils not suited for agricultural uses (vulnerability¼2).

2.3 Rainfall intensity

This indicator evaluates the intensity of precipitation in a period of time along a watershed. Accordingly to Crepani et al (Crepani

et al., 2004), high precipitation in a small period of time

increases runoff, leading to soil erosion and compaction. Thus, high rainfall intensity in a region contributes to its higher sensitivity and vulnerability. Rainfall in semi-arid regions is usually concentrated in time.

Rain intensityi ¼

ðPnj¼1Annual pluviometryj ðDays with rainfall30

Þ

n

Where‘Annual_pluviometryj’means the millimeters of rainfall in year‘j’;‘n’is the number of years of historical data;‘Days_with_rainfallj’is the number of days that rained in year‘j’and‘Rainfall_intensityi’is the rainfall intensity measured in site

‘i’, located in or near the studied watershed. The representative area of a particular meteorological monitoring point in a watershed can be measured by calculating Thiessen polygons in the watershed map, according to Miranda

(Miranda, 2005).

M.C. Brito de Figueirêdo et al. / Journal of Cleaner Production 18 (2010) 1376e1385

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(continued)

Indicators Description Calculation procedure 2.4 Irrigation

water quality

This indicator represents the water quality available for irrigation in a watershed, considering its salinity and sodicity. The risk of soil salinization or sodification is given by potential of water to cause these effects, according to Ayres and Westcot

(Ayers and Westcot, 1991). Thus, a higher water salinity or

sodicity leads to a higher watershed vulnerability to soil salinization or sodification.

Irrigation water quality is based on two parameters: water salinity, evaluated measured electric conductivity (EC); and water sodicity, evaluated by measuring water EC and the sodium adsorption ratio (SAR). A watershed whose water bodies present high EC is more sensitive and vulnerable to soil salinization. When water bodies present low EC for a given SAR, the watershed is more sensitive to soil sodification(Ayers and

Westcot, 1991).

Water salinity ranges from 0.1 (minimum value) to 3 dS m 1(maximum value),

according to the range of values observed by Ayres and Westcot(Ayers and

Westcot, 1991).

The vulnerability to soil sodification in a particular watershed area, influenced by a water monitoring point, is calculated according toAyres and Westcot (1991), combining EC and SAR values:

- Vulnerability¼1 (low): when SAR 0 to 3 and EC>0.7; SAR 3 to 6 and EC>1.2;

SAR 6 to 12 and EC>1.9; SAR 12 to 20 and EC>2.9; and, SAR 20 to 40 and EC>5;

- Vulnerability¼1.5 (medium): when SAR 0 to 3 and EC 0.7 to 0.2; SAR 3 to 6 and EC 1.2 to 0.3; SAR 6 to 12 and EC 1.9 to 0.5; SAR 12 to 20 and EC 2.9 to 1.3; and, SAR 20 to 40 and EC 5 to 2.9;

- Vulnerability = 2 (very high): when SAR 0 to 3 and EC<0.2; SAR 3 to 6 and

EC<0.3; SAR 6 to 12 and EC<0.5; SAR 12 to 20 and EC<1.3; and, SAR 20 to 40

and EC<2.9.

The influence area of a particular EC and SAR monitoring point in a watershed can be measured by calculating Thiessen polygons, according to Miranda (Miranda, 2005). Thefinal normalized vulnerability value in each watershed area is obtained by the arithmetic average of salinity and sodicity vulnerability values obtained for that area.

2.5 Climate aridity

This indicator evaluates the average climate class of a watershed. Regions in arid and semi-arid climates are more sensitive and vulnerable to water scarcity, soil salinization and sodification, and desertification processes. The United Nations uses this indicator to identify areas susceptible to water scarcity and desertification (MMA, 2004).

Watershed vulnerability is evaluated by attributing different vulnerability values to each one of its areas dominated by climate risk classes: area with arid climate (vulnerability¼2); area with semi-arid climate (vulnerability¼1.8); area with sub-humid dry climate (vulnerability¼1.6); areas surrounding arid and semi-arid climate (vulnerability¼1.4); areas with other climates (vulnerability¼1). 3.1 Areas within

conservation units

This indicator evaluates the commitment of municipalities in a watershed with conservation of natural habitats. Areas legally protected receive formal intervention preventing natural resource exploitation.

Three conservation area types are considered: integral protection areas, where very limited human intervention is allowed (vulnerability¼1.2); areas partially

protected, where some human intervention is allowed (vulnerability¼1.6); areas not protected, where any human intervention is allowed (vulnerability¼2). 3.2 Adoption of

conservation practices

This indicator evaluates the use of soil and vegetation conservation practices in a watershed. Adoption of conservation practices is measured considering the following actions in the watershed: deforestation prevention; degraded forests recovery; salinization, compaction and erosion processes’

prevention or remediation; agrochemical use control, and use of agricultural practices to prevent organic soil matter losses.

When all these actions are carried out, it is assumed that the local community is aware of conservation practices and its capacity of response is high

(vulnerability¼1). Conversely, if none of these actions is adopted, the community has low capacity of response (vulnerability¼2). If some of these actions are

adopted, capacity of response is considered medium (vulnerability¼1.5). These

data can be gathered from the municipalities’agriculture agencies, at the watershed management committee, or at government published researches. 3.3 Potable

water

This indicator evaluates the access of the population living in a watershed to potable (properly treated) water, by measuring two parameters: access to water distribution system (the percentage of population with access to water distribution system) and access to treated water (the percentage of the total distributed water that was properly treated). These parameters are complementary, since a wide access to a water distribution system does not guarantee proper treatment, which involves at least the processes offlocculation, decantation,filtration, and disinfection. Access to potable water is important to human health, especially where water bodies receive improperly treated effluents.

Access_water_distributioni¼(population_accessi(hab)/

population_municipalityi(hab))*100

Where‘population_accessi’is the number of people in municipality‘i’with access to water distribution system;‘population municipalityi’is the number of people in municipality‘i’, and‘Access_water_distributioni’is the percentage of the

population with access to water distribution system in municipality‘i’. Treated_wateri¼(volume_water_treatedi(m3/day)/

volume_water_distributedi(m3/day))*100

Where‘volume_water_treatedi’is the volume of water that received appropriate treatment (at leastflocculation, decantation,filtration, and disinfection) in municipality‘i’in the studied watershed;‘volume_water_distributedi’is the volume of water distributed by the public system in municipality‘i’; ‘Treated_wateri’is the percentage of the distributed water that was properly

treated in municipality‘i’. These data are usually available in water distribution agencies or government offices.

Both parameters (Access_water_distributioni’and‘Treated_wateri’) range from 0% (minimum value) to 100% (maximum value) in a municipality. Thefinal vulnerability value of this indicator in a municipality is obtained by calculating the average mean of the normalized vulnerability values related to both analyzed parameters.

3.4 Solid waste disposal

This indicator evaluates the population access to solid wastes collection and to adequate disposal, by measuring two parameters: access to solid wastes collection and access to appropriate disposal. Solid wastes not collected or inadequately disposed cause soil and water contamination, as well as air pollution. An efficient solid waste management system indicates a good capacity of response of local society to minimize the potential impact of solid wastes generation.

Access_collectioni¼(population_reachedi/population_municipalityi)*100

Where‘population_reachedi’is the number of people served by a solid wastes collection system in a municipality‘i’;‘population municipalityi’is the number of people in municipality‘i’, and‘Access_collectioni’is the percentage of the population with access to solid wastes collection system in municipality‘i’. Solidwaste_disposali¼mass_appropriately_disposedi(t/day)/mass_collectedi(t/

day)*100

Where‘mass_appropriately_disposedi’is the mass of solid wastes sent to recycling, composting, incineration or controlled landfill in municipality‘i’;

‘mass_collectedi’is the mass of solid wastes collected by the public system in a municipality‘i’, and‘Solidwaste_disposali’is the percentage of the total mass of solid wastes collected that was sent to an appropriate disposal in municipality‘i’. These data are usually available in the public agencies responsible for sanitation activities.

Both parameters (Access_collectioni’and‘Solidwaste_disposali’) range from 0%

(minimum value) to 100% (maximum value) in a municipality. Thefinal vulnerability value of this indicator in a municipality is obtained by calculating the average mean of the normalized vulnerability values related to the two analyzed parameters.

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References

Rodrigues, G.S., Campanhola, C., Kitamura, P.C., 2003. An environmental impact assessment system for agricultural R&D. Environmental Impact Assessment Review 23, 219e244.

Jesus-Hitzschky, K.R.E., 2007. Impact Assessment system for technological innovation: INOVA-tec system. Journal of Technology Management & Innovation 2 (2), 67e82.

Goedkoop, M., Spriensma, R., 2000. The Eco-indicator 99: a Damage Oriented Method for Life Cycle Impact Assessment. Methodology Report. Amersfoort: PRé Consultants B.V..

Jolliet, O., Margni, M., Charles, R., Humbert, S., Payet, J., Rebitzer, G., Rosenbaum, R., 2003. IMPACT 2002þ: a new life cycle impact assessment methodology. Journal of LCA 8 (6), 324e330.

Steen, B., 1999. A systematic approach to environmental priority strategies in product development (EPS). Version 2000eGeneral system characteristics.

http://msl1.mit.edu/ind_eco/sslinks/links.php?go¼20.

Bare, J.C., Norris, G.A., Pennington, D.W., McKone, T., 2003. TRACI: the tool for the reduction and assessment of chemical and other environmental impacts. Journal of Industrial Ecology 6 (3e4), 49e73.

Potting, J., Hauschild, M., 2005. Background for spatial differentiation in LCA impact assessment - The EDIP 2003 methodology. http://www2.mst.dk/Udgiv/

publications/2005/87-7614-581-6/pdf/87-7614-582-4.pdf.

Kværner, J., Swensen, G., Erikstad, L., 2006. Assessing environmental vulnerability in EIA: the content and context of the vulnerability concept in an alternative approach to standard EIA procedure. Environmental Impact Assessment Review 26, 511e527.

Metzger, M.J., Rounsevell, M.D.A., Acosta-Michlik, L., Leemans, R., Schoter, D., 2006. The vulnerability of ecosystems services to land use change. Agriculture, Ecosystems & Environment 114 (1), 69e85.

Li, A., Wang, A., Liang, S., Zhou, W., 2006. Eco-environmental vulnerability evalua-tion in mountainous region using remote sensing and GISea case study in the

upper reaches of Minjiang River, China. Ecological Modeling 192, 175e187.

Barreto, FMS. Groundwater Contamination by Pesticides and Nitrate at Tianguá, Ceará. PhD Thesis [in Portuguese], Fortaleza: Ceará Federal University; Hydraulics and Environmental Engineering, Brazil, 2006.

Lima, L.C., Morais, J.O., Souza, M.J.N., 2000. Compartimentação Territorial e Gestão Regional do Ceará. UNECE, Fortaleza.

Schoter, D., Metzger, M.J., Cramer, W., Leemans, R., 2004. Vulnerability assessmente

analysing the human-environment system in the face of global environmental change. ESS Bulletin 2 (2), 11e17.

Tixier, J., Dandrieux, A., Dussere, G., Bubbico, R., Mazarotta, B., Silvetti, B., Hubert, E., Rodrigues, N., Salvi, O., 2005. Environmental vulnerability assessment in the vicinity of an industrial site in the frame of ARAMIS European project. Journal of Hazardous Materials 130 (3), 251e264.

(continued)

Indicators Description Calculation procedure 3.5 Wastewater

treatment

This indicator evaluates the percentage of the population that has access to wastewater treatment system in a watershed. The population access to a wastewater treatment system is an initial response to reduce sewage pressure on water bodies in a watershed.

Access_wastewater_systemi¼(poulation_reachedi(hab)/

poulation_municipalityi(hab))*100

Where,‘population_reachedi’is the number of people with access to wastewater treatment system in a municipality‘i’;‘population_municipalityi’is the number of

people in municipality‘i’, and‘Access_wastewater_systemi’is the percentage of

the population in municipality‘i’that has access to a wastewater treatment system. This indicator ranges from 0% (minimum value) to 100% (maximum value) in a municipality.

3.6 Surface water availability per person

This indicator evaluates surface water availability in a watershed. When a watershed has a management plan, investments are directed to increase surface water availability through construction of wells, dams and other equipment that allow water storage and supply in all seasons. Thus, higher surface water availability per person in a watershed is related to society capacity of response to prevent water scarcity events.

Water_availability¼average_flow(m3/year)/watershed_population(hab)

Where‘average_flow’is the long-term annual averageflow of the main river in a watershed (available 90% of the time in regulated rivers and in 95% of the time in non-regulated perennial rivers);‘watershed_population’is the population of

a watershed; and‘Water_availability’is the volume of water yearly available per person in a watershed. These data are commonly available in watershed management plans or at national and state water management agencies.

Appendix B. Data sources to the proposed indicators and suggestions of minimum and maximum values to Brazil.

Indicators Minimum value Maximum value Data sources

1.1 Agricultural activities 0% 100% Agriculture Census (IBGE, 1996) and Demographic Census (IBGE, 2000) 1.2 Industrial activities 0 employees/km2 125 employees/km2 Central Companies Register (IBGE, 2009)

1.3 Wastewater generation per person 10 m3.hab 1.year 1 100 m3.hab 1.year1 National Basic Sanitation Research (IBGE, 2000), National Basic

Sanitation Information System - SNIS (SNIS, 2005).and Demographic Census (IBGE, 2000)

1.4 Waste generation per person 0.1 kg.hab1.day1 1.5 kg.hab1.day1 National Basic Sanitation Research (IBGE, 2000), National Basic

Sanitation Information System - SNIS (SNIS, 2006) and Demographic Census (IBGE, 2000)

1.5 Water demand per person 30 m3.hab 1.year1 1.500 m3.hab1.year1Database of the Water National Agency - ANA (ANA, 2005), research

performed by

Rebouças (Rebouças, 2002) and Demographic Census (IBGE, 2000) 2.1 Priority areas for conservation* 1 2 Map of Priority Areas for Conservation(MMA, 2006)

2.2 Agriculture suitability* 1 2 Studies of agriculture suitability (Ministério da Agricultura, 1979)

2.3 Rainfall intensity 50 mm month1 525 mm month1 Northeast Hidroclimatic Database (SUDENE, 2008)

2.4 Irrigation water quality* 0.1 dS m 1(water

salinity)

3 dS m1(water

salinity)

Database of the Water National Agency - ANA (ANA, 200))

2.5 Climate aridity* 1 2 Map of Areas susceptible to desertification in Semi-Arid (MMA, 2004) 3.1 Areas within conservation units* 1.2 2 Map of Protected Areas (IBAMA, 2009)

3.2 Adoption of conservation practices* 1 2 Brazilian Municipalities Prole (IBGE, 2002)

3.3 Potable water 0% 100% National Basic Sanitation Research (BGE, 2000) and Demographic Census (IBGE, 2000)

3.4 Solid waste disposal 0% 100% National Basic Sanitation Research (IBGE, 2000) and Demographic Census (IBGE, 2000)

3.5 Wastewater treatment 0% 100% National Basic Sanitation Research (IBGE, 2000) and Demographic Census (IBGE, 2000)

3.6 Water availability per person 0 m3.hab 1year 1 100.000 m3.

hab1year 1 Database of the Water National Agency - ANA (Demographic Census (IBGE, 2000) ANA, 2006) and

3.7 Municipality Human Development Index (HDI-M)

1 2 Brazilian Atlas of Human Development (PNUD, 2003)

*Qualitative indicators with ranges described inAppendix A.

M.C. Brito de Figueirêdo et al. / Journal of Cleaner Production 18 (2010) 1376e1385

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Tran, L.T., Knight, C.G., O’neill, R., Smith, E.R., Riitters, K.H., Wickham, J., 2002.

Environmental assessment: fuzzy decision analysis for integrated environ-mental vulnerability assessment of the mid-Atlantic region. Environenviron-mental Management 29 (6), 845e859.

Zielinski, J., 2002. Watershed Vulnerability Analysis. Center for Watershed Protec-tion, EllicotCity.http://www.cwp.org.

Bennion, H., Hilton, J., Hughes, M., Clark, J., Hornby, D., Fozzard, I., Phillips, G., Reynolds, C., 2005. The use of a GIS-based inventory to provide a national assessment of standing waters at risk from eutophication in Great Britain. Science of the Total Environment 344, 259e273.

Figueirêdo, M.C.B., Teixeira, A.S., Araujo, L.F.P., Rosa, M.F., Paulino, W.D., Mota, S., Araujo, J.C., 2007. Evaluation of reservoirs environmental vulnerability to eutrophication. Engenharia Sanitária e Ambiental 12 (4), 399e409.

Villa, F., McLeod, H., 2002. Environmental vulnerability indicators for environ-mental planning and decision-making: guidelines and applications. Environ-mental Management 29 (3), 335e348.

Adger, W.N., 2006. Vulnerability. Global Environmental Change 16, 268e281.

Gallopin, G.C., 2006. Linkages between vulnerability, resilience, and adaptive capacity. Global Environmental Change 16, 293e303.

Figueirêdo, M.C.B. Environmental Performance Evaluation of Agro-Industrial Technological Innovations, Considering Life Cycle and Environmental Vulnera-bility: Ambitec-Life Cycle Model. PhD Thesis [in Portuguese]. Fortaleza: Ceará Federal University; Hydraulics and Environmental Engineering, Brazil, 2008. Monteiro, R.C., Rodrigues, G.S., 2006. A system of integrated indicators for

socio-environmental assessment and eco-certification in agricultureeambitec-agro.

Journal of Technology Management and Innovation 1 (3), 47e59.

Figueirêdo, M.C.B., Mota, F.S.B., Rodrigues, G.S., Caldeira-Pires, A., Rosa, M.F., Vieira, V.P.P.B. Method for Considering Life Cycle Thinking and Watershed Vulnera-bility Analysis in the Environmental Performance Evaluation of Agro-Industrial Innovations (Ambitec-Life Cycle). In: Proceedings of the 6th Int. Conf. on LCA in the Agri-Food Sector. Zurich, 2009: 159e168.

Malczewski, J., 1999. GIS and Multi-Criteria Decision Analysis. John Wiley & Sons, Nova York.

Canter, L.W., 1996. Environmental Impact Assessment. McGraw-Hill, Nova York. Sistema Nacional de Informações sobre Saneamento (SNIS) [National Sanitation

Information System], 2005. Diagnóstico dos Serviços de Água e Esgoto. Minis-tério das Cidades, Brasília.http://www.snis.gov.br.

Instituto Brasileiro de Geografia e Estatística (IBGE) [Brazilian Institute for Geog-raphy and Statistics], 2000. Pesquisa Nacional de Saneamento Básico 2000. IBGE, Brasília.http://www.ibge.gov.br.

Ministério do Meio Ambiente (MMA) [Brazilian Environmental Ministry], 2006. Mapa das Áreas Prioritárias para conservação, uso sustentável e repartição de benefícios da biodiversidade brasileira. MMA, Brasília.http://www.mma.gov.br. Crepani, E., Medeiros, J.S., Palmeira, A.F., 2004. Intensidade pluviométrica: uma maneira de tratar dados pluviométricos para análise da vulnerabilidade de paisagens à perda de solo. São José Dos Campos: Inpe.

Miranda, J.I., 2005. Fundamentos de Sistemas de Informações Geográficas. Embrapa, Brasília.

Ayers, R.S., Westcot, D.W., 1991. A qualidade da água na agricultura. UFPB, Campina Grande.

Ministério do Meio Ambiente (MMA) [Brazilian Environmental Ministry], 2004. Mapa de Áreas Susceptíveis à Desertificação 2004. MMA, Brasília.http://www.

mma.gov.br.

Instituto Brasileiro de Geografia e Estatística (IBGE) [Brazilian Institute for Geog-raphy and Statistics], 1996. Censo Agropecuário 1996. IBGE, Brasília. http://

www.ibge.gov.br.

Instituto Brasileiro de Geografia e Estatística (IBGE) [Brazilian Institute for Geog-raphy and Statistics], 2000. Censo Demográfico 2000. IBGE, Brasília.http://

www.ibge.gov.br.

Instituto Brasileiro de Geografia e Estatística (IBGE) [Brazilian Institute for Geog-raphy and Statistics], 2009. Mapa de Áreas Protegidas 2009. IBGE, Brasília.

http://mapas.ibge.gov.br/uc/Run.htm.

Sistema Nacional de Informações sobre Saneamento (SNIS) [National Sanitation Information System], 2006. Diagnóstico do manejo de resíduos sólidos urbanos 2006. Ministério das Cidades, Brasília.http://www.snis.gov.br.

Agência Nacional de Águas (ANA) [Brazilian National Water Agency], 2005. Cadernos de recursos hídricos: disponibilidades e demandas de recursos hídricos no Brasil.http://www.ana.gov.br.

Rebouças, A.C., 2002. Água doce no mundo e no Brasil. In: Rebouças, A.C., Braga, B., Tundisi, J.G. (Eds.), Águas doces no Brasil: capital ecológico, uso e conservação. São Paulo: Escrituras Editora, pp. 01e37.

Ministério da Agricultura, Pecuária e Abastecimento (MAPA), 1979. Aptidão Agrícola das Terras do Ceará. MMA/SUPLAN, Brasília.

Superintendência do Desenvolvimento do Nordeste (SUDENE) [Northeast Devel-opment Agency], 2008. Rede Hidroclimática do Nordeste (1963e1973).http://

pageserver-nt.sudene.gov.br/ixpress/pluviometria/plv/index.dml.

Agência Nacional de Águas (ANA) [Brazilian National Water Agency], 2006. Banco de dados dos recursos hídricos no Brasil. ANA, Brasília.

Instituto Brasileiro de Meio Ambiente e Recursos Naturais Renováveis (IBAMA), 2009. Federal Conservation Units 2009. IBAMA, Brasília.http://www.ibama.gov.

br/patrimonio/.

Instituto Brasileiro de Geografia e Estatística (IBGE) [Brazilian Institute for Geog-raphy and Statistics], 2002. Pesquisa Perfil dos Municípios BrasileiroseMeio

Ambiente: instrumentos de gestão ambiental 2002. IBGE, Brasília.http://www.

ibge.gov.br/munic_meio_ambiente_2002/index.htm.

United Nations Development Program (PNUD), 2003. Brazilian Atlas of Human Development. PNUD, Salvador.http://www.pnud.org.br/atlas/.

Maria Cléa Brito de Figueirêdo: Ph.D in Civil Engineering, in the concentration area of

Environmental Sanitation, from Ceará Federal University (Fortaleza, Ceará, Brazil) in 2009. M.Sc in Science and Technology Studies from Rensselaer Polytechnic Institute (Troy, NY, USA) in 2003. Researcher at the Brazilian Agency for Agricultural Researche

Embrapa, Center of Tropical Agro-industry, since 2001, in the area of environmental impact assessment.

Geraldo Stachetti Rodrigues: Ph.D. in Ecology and Evolutionary Biology from Cornell

University (Ithaca, NY, USA) in 1995. Researcher at the Brazilian Agency for Agricultural ResearcheEmbrapa, Center of Environment, in the area of environmental impact

assessment since 1987. Currently working at Embrapa Labex Europe/Unité Perfor-mance des systèmes de culture de plantes pérennes (Montpellier, France).

Armando Caldeira-Pires: Ph.D. in Mechanical Engineering from LisboaTechnical

University in 1995. Professor at the Brasília University (Brasília, DF, Brazil) since 2004. Project leader and professor in the areas of industrial ecology and life cycle analysis.

Morsyleide de Freitas Rosa: Ph.D in Chemical Engineering from Rio de JaneiroFederal

University. Researcher at the Brazilian Agency for Agricultural ResearcheEmbrapa,

Center of Tropical Agro-industry, since 1995, in the areas of environmental manage-ment and nanotechnology.

Fernando Antônio Sousa de Aragão: M.Sc. in Plant Breading from Viçosa Federal

University (Viçosa, MG, Brazil) in 1997. Researcher at the Brazilian Agency for Agri-cultural ResearcheEmbrapa, Center of Tropical Agro-industry, since 2002, in the area

of agronomy.

Vicente de Paulo Pereira Barbosa Vieira: Ph.D in Water Resources Management from

ColoradoUniversity in 1978. Professor at Ceará Federal University since 1963 in the areas of water resources economy, risk analysis and environmental indicators building.

Francisco Suetônio Bastos Mota: Ph.D. in Public Health from São Paulo University

Imagem

Fig. 1. Set of watershed environmental vulnerability indicators, organized in a multi-criteria structure.
Fig. 4. Environmental performance along the life cycle stages of GCS and RCS without and with vulnerability analysis integration.

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Ousasse apontar algumas hipóteses para a solução desse problema público a partir do exposto dos autores usados como base para fundamentação teórica, da análise dos dados

Somado a isto, faz-se imprescindível ressaltar a importância de se compreender corretamente o funcionamento e a sensibilidade de equipamentos de avaliação da condição “in situ”

Findando o mapeamento teórico sobre inclusão digital em Portugal, destacamos principalmente a compreensão destes trabalhos pela literacia digital (PEREIRA, 2012;

Os diretórios de empresas são bases de dados de empresas com um papel bastante importante, tanto no âmbito de prospeção de mercado como na divulgação da empresa. São

Mas, a construção do espaço não seria tão convincente se não tivessem utilizado recursos identitários capazes de gerar efeitos de inclusão e exclusão, circunstância pela qual

Therefore, for the understanding of trauma as an open concept, and open concept such as that which is illustrated by the use of the terms reality, or life, such argument needs to